Advanced Industrial SCIence (산업과 과학)

Convergence Society for SMB (중소기업융합학회)
권호 표지
DOI QR코드

DOI QR Code

Development of AI Convergence Education Model Based on Machine Learning for Data Literacy

데이터 리터러시를 위한 머신러닝 기반 AI 융합 수업 모형 개발

  • Sang-Woo Kang (Department of AI Convergence Education, Seoul National University) ;
  • Yoo-Jin Lee (Department of AI Convergence Education, Seoul National University) ;
  • Hyo-Jeong Lim (Department of AI Convergence Education, Seoul National University) ;
  • Won-Keun Choi (Department of AI Convergence Education, Seoul National University)
  • 강상우 (서울대학교 AI융합교육학과) ;
  • 이유진 (서울대학교 AI융합교육학과) ;
  • 임효정 (서울대학교 AI융합교육학과) ;
  • 최원근 (서울대학교 AI융합교육학과)
  • Received : 2023.12.19
  • Accepted : 2024.03.20
  • Published : 2024.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this study is to develop a machine learning-based AI convergence class model and class design principles that can foster data literacy in high school students, and to develop detailed guidelines accordingly. We developed a machine learning-based teaching model, design principles, and detailed guidelines through research on prior literature, and applied them to 15 students at a specialized high school in Seoul. As a result of the study, students' data literacy improved statistically significantly (p<.001), so we confirmed that the model of this study has a positive effect on improving learners' data literacy, and it is expected that it will lead to related research in the future.

본 연구는 고등학교 학생들의 데이터 리터러시를 함양할 수 있는 머신러닝 기반 AI 융합 수업 모형과 수업 설계 원리를 개발하고, 그에 따른 상세 지침을 개발하는 것을 목적으로 하였다. 이를 위해 선행 문헌 연구를 통해 머신러닝을 기반으로 한 수업 모형과 설계 원리 및 상세 지침을 개발하고, 서울 소재 상업계열 특성화고등학교 학생 15명에게 적용하여 실행하였다. 연구 결과 학생들의 데이터 리터러시가 통계적으로 유의미(p< .001)하게 향상되었으므로 본 연구의 수업 모형이 학습자의 데이터 리터러시 향상에 긍정적인 영향을 주었음을 확인할 수 있었고, 앞으로 관련 연구로 이어지길 기대한다.

Keywords

References

  1. Kim, S. H. (2019). Analysis of International Educational Trends and Learning Tools for Artificial Intelligence Education. The Journal of Korean Association of Computer Education, 23(2), 25-28.
  2. Lin, P., & Brummelen, J. V. (2021). Engaging Teachers to Co-Design integrated AI Curriculum for K-12 Classrooms. CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS. NEW YORK: Assoc Computing Machinery. DOI : 10.1145/3411764.3445377
  3. The Government of the Republic of Korea. (2020). Education policy direction and key tasks in the artificial intelligence era.
  4. Ministry of Education. (2022). The 2022 revised curriculum.
  5. Lee, D. Y., & Han, S. K. (2022). Analysis of changes in AI competency, attitude, and perception through development and application of AI education program. Journal of the Korean Association of Artificial Intelligence Education, 3(3), 7-14. DOI : 10.52618/aied.2022.3.3.2
  6. Kim, S. M., & Park, K. B. (2023). The effectiveness of K-means clustering algorithm-based class in elementary social education. Korean Association For Learner-Centered Curriculum And Instruction, 23(5), 127-140. DOI : 10.22251/jlcci.2023.23.5.127
  7. Hwang, H. S. (2016). Study on Big Data Utilization in Social Studies Education. Social Studies Education, 55(3), 75-89.
  8. Wolff, A., Gooch, D., & Kortuem, G. (2016). Data Literacy to Support Human-centred Machine Learning. In: CHI 2016, 7-12 May 2016, San Jose California, USA. http://www.doc.gold.ac.uk/~mas02mg/HCML2016/HCML2016_paper_1.pdf
  9. Song, Y. K. (2021). The Data-Driven Debate (DDD) Instructional Model for Improving Data Literacy. Master's thesis. Seoul National University, Seoul.
  10. Cho, Y. S. (2022). Effects of AI Convergence Science Classes on Promoting Middle School Students' Attitude Towards AI Technology and Data Literacy. Master's thesis. Ewha Womans University, Seoul.
  11. Jeong, J. H., Kim, J. Y., & Kim, K. H. (2022). A Research on the Design and Application of a Machine Learning Project Class Programs in Artificial Intelligence Education in High School. The Journal of Korean Association of Computer Education. 26(1), 203-204.
  12. Olari, V., & Romeike, R. (2021). Addressing AI and Data Literacy in Teacher Education: A Review of Existing Educational Frameworks. In Proceedings of the 16th Workshop in Primary and Secondary Computing Education (WiPSCE '21). Association for Computing Machinery, New York, NY, USA, Article 17, 1-2. DOI : 10.1145/3481312.3481351
  13. Jarrahi, M. H., Memariani, A., & Guha, S. (2022). The Principles of Data-Centric AI (DCAI). Communications of the ACM, 66(8), 84-92. DOI : 10.1145/3571724
  14. Strickland, E. (2022). Andrew Ng, AI Minimalist: The Machine-Learning Pioneer Says Small is the New Big. In IEEE Spectrum, 59(4), 22-50. DOI : 10.1109/MSPEC.2022.9754503
  15. AI4K12 (2021). Grade Band Progression Charts. https://ai4k12.org/gradeband-progression-charts/
  16. Actua (2022). Actua's Artificial Intelligence (AI) Education Handbook. CIRA. https://actua.ca/wp-content/uploads/2023/07/AI_Handbook_2023_V2.pdf
  17. Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Conference on Human Factors in Computing Systems - Proceedings. NEW YORK: Assoc Computing Machinery. DOI : 10.1145/3313831.3376727
  18. Wolff, A., Gooch, D., Cavero Montaner, J. J., Rashid, U., & Kortuem, G. (2016). Creating an Understanding of Data Literacy for a Data-driven Society. The Journal of Community Informatics, 12(3), 9-26. DOI : 10.15353/joci.v12i3.3275
  19. Lee, W. T. (2015). Overcoming Information Disparities in the Era of Big Data through Data Literacy. KISO JOURNAL, 21. https://journal.kiso.or.kr/?p=7012%202015.12.21
  20. Han, S. W. (2018). A Study about the Concept of Data Literacy based on Digital Humanities. Journal of the Korean Society for Information Management, 35(4), 223-236. DOI : 10.3743/KOSIM.2018.35.4.223
  21. Bae, H. S. (2019). Educational Implications of Data Literacy in Social Studies. Theory and Research in Citizenship Education, 51(1), 95-120. DOI : 10.35557/trce.51.1.201903.004
  22. Lee, E. J. (2022). The Effects of AI-based Data Analysis Education on Convergent Thinking Ability and Data Literacy of General High School Students. Master's thesis. Kongju National University, Gongju.
  23. Korea Institute for Health and Social Affairs, Oh, M. A., Choi, H. S., Kim, S. H., Chang, J. H., Jin, J. H. & Cheon, M. K. (2017). A Study on Social security Big Data Analysis and Prediction Model based on Machine Learning. (2017-46). https://www.kihasa.re.kr/publish/report/view?seq=27848
  24. Cho, M. H. (2021). A Study on the History, Classification and Development Direction of Artificial Intelligence. Journal of The Korea Institute of Electronic Communication Sciences, 16(2), 307-312. DOI : 10.13067/JKIECS.2021.16.2.307
  25. Mariescu-Istodor, R., & Jormanainen, I. (2019). Machine Learning for High School Students. 19TH KOLI CALLING CONFERENCE ON COMPUTING EDUCATION RESEARCH (KOLI CALLING 2019). NEW YORK: Assoc Computing Machinery. DOI : 10.1145/3364510.3364520
  26. Kim, D. Y., Chae, D. Y., & Park, S. H. (2023). Development and application of PBL-based machine learning education program to improve elementary school students' problem solving skills. Korean Association For Learner-Centered Curriculum And Instruction, 23(6), 639-661. DOI : 10.22251/jlcci.2023.23.6.639
  27. Moon, W. J. et al. (2021). Effect of Machine Learning Education Focused on Data Labeling on Computational Thinking of Elementary School Students. Journal of The Korean Association of Information Education, 25(2), 327-335. https://doi.org/10.14352/jkaie.2021.25.2.327
  28. A Study on how to apply AI education to K-12.(2023). Seoul. Korea Foundation for the advancement of Science & Creativity.
  29. Korea Institute for Curriculum and Evaluation, Hong, S. J. et al. Artificial Intelligence and EduTech in School Education. (RRI 2020-2). https://www.kice.re.kr/
  30. Kwon, H. S. et al. (2021). Current Status of the Implementation of Convergence Education in Primary and Secondary Schools. Journal of Science Education, 45(3), 336-348. DOI : 10.21796/jse.2021.45.3.336
  31. Park, J. Y. (2023). Analysis of Attitude Toward AI According to SW Non-major's Computational Thinking and AI Experience. The Journal of Korean Association of Computer Education, 26(1), 33-41. DOI : 10.32431/kace.2023.26.1.004
  32. Leem, J. H. (2018). Main Issues of Software Education and Tasks of Educational Technology for improving Software Education. Journal of Educational Technology, 34(3), 679-709. DOI : 10.17232/KSET.34.3.679
  33. Noh, D. K. (2023). Development and application of artificial intelligence (AI) and high school science integrated education programs based on scientific data. Master's thesis. Seoul National University, Seoul.
  34. Lee, J. H., Jo, J. H., & Chae, S. C (2021). Development of Data-driven Teaching Material for AI Convergence Education: Focused on Damped Oscillation. School Science Journal, 15(2), 121-134. https://doi.org/10.15737/SSJ.15.2.202105.121
  35. Lim, C. I. (2012). Curriculum Design Theories and Models. Seoul : KYOYOOKBOOK.
  36. Mandinach, E. B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 366-376. DOI : 10.1016/j.tate.2016.07.011
  37. Kim, B. C., Kim, B. S., & Kim, J. H. (2022). Development and Validation of Data Science Education Instructional Model. Journal of The Korean Association of Information Education, 26(5), 417-425. DOI : 10.14352/jkaie.2022.26.5.417
  38. Wolff, A., Wermelinger, M., & Petre, M. (2019). Exploring design principles for data literacy activities to support children's inquiries from complex data. International Journal of Human-Computer Studies, 129, 41-54. DOI : 10.1016/j.ijhcs.2019.03.006
  39. Jonassen, D. (1999). 10 Designing Constructivist Learning Environments. Instructional-design theories and models, 11, 21. https://www.davidlewisphd.com/courses/EDD8121/readings/1999-Jonassen.pdf
  40. Son, M. H., & Jeong, D. H. (2020). Development of Data-Driven Science Inquiry Model and Strategy for Cultivating Knowledge-Information-Processing Competency. Journal of the Korean Association for Science Education, 40(6), 657-670. DOI : 0.14697/jkase.2020.40.6.657 https://doi.org/10.14697/jkase.2020.40.6.657
  41. Seo, Y. N., Noh, J. Y., Park, M. R., & Jung, S. J. (2023). A Developmental study of an Instructional Model for Inquiry of Social Studies Based on Data-driven Artificial Intelligence Convergence Education. Korean Association For Learner-Centered Curriculum And Instruction, 23(12), 1-25. DOI : 10.22251/jlcci.2023.23.12.1
  42. Hong, H. W. (2020). Development of the Design Principles of Constructivist Learning Environment for Teaching Pre-Service Teachers in Elementary School Teaching Practices. Dctoral dssertation. Jeonbuk National University, Jeollabuk-do.
  43. Chee, H. K., & Lim, C. I. (2022). A Study on Development of an Instructional Design Principles of Integrating Subject Matter and Software for Creative Problem Solving. Journal of Educational Technology, 38(2), 369-407. DOI : 10.17232/KSET.38.2.369
  44. Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563-575. DOI : 10.1111/j.1744-6570.1975.tb01393.x
  45. Grant, J. S., & Davis, L. L. (1997). Selection and use of content experts for instrument developme nt. Research in nursing & health, 20(3), 269-274. DOI : 10.1002/(SICI)1098-240X(199706)20:3<26 9::AID-NUR9>3.0.CO;2-G
  46. Lynn, M. R. (1986). Determination and quantification of content validity. Nursing research (New York), 35(6), 382-386. DOI : 10.1097/00006199-198611000-00017